Using unmanned aerial systems and deep learning for agriculture mapping in Dubai

Abstract

Dubai’s ‘Sustainable Future’ vision prioritizes Sustainable Agriculture as a key pillar of its ‘Food Security Strategies’. To boost productivity and efficiency, Dubai Emirate has adopted advanced technologies. Accurate land monitoring is crucial for effective food security control and support measures. However, traditional methods relying on costly and time-consuming field surveys conducted by experts are limited in scope. To address this, affordable and efficient agriculture mapping relies on remote sensing through drone surveys. Dubai Municipality utilizes Unmanned Aerial Vehicles (UAVs) to map farming areas across the Emirate, identify cultivable lands, and establish a precise agriculture database. A study conducted over 6 months used Trimble UX5 (HP) drones for high-resolution imaging in 12 Dubai communities. It employed novel object detection methods and geospatial analysis. Deep learning models achieved 85.4% accuracy in vegetation cover and F1-scores of 96.03% and 94.54% for date palms and GHAF trees, respectively, compared to ground truth data. This research highlights the potential of UAVs and deep learning algorithms for large-scale sustainable agricultural mapping. By providing specialists with an integrated solution to measure and assess live green vegetation cover derived from processed images, it contributes to the advancement of sustainable agriculture practices.